基于人工智能的智能港口物流 Metaverse,用于提高港口物流的生产率、环境和安全:釜山港案例研究

Sunghyun Sim, Dohee Kim, Kikun Park, Hyerim Bae
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摘要

全球贸易的增长、COVID-19 的影响以及环境和安全法规的收紧给海运市场带来了重大变化。为了应对这些挑战,港口物流业正在迅速采用大数据、物联网和人工智能等先进技术。然而,尽管做出了这些努力,要解决港口物流业中与生产率、环境和安全相关的若干问题,仍需要各利益相关方的通力合作。在本研究中,我们介绍了一个基于人工智能的港口物流元宇宙框架(PLMF),该框架可促进港口物流领域不同利益相关者之间的交流、数据共享和决策制定。所开发的 PLMF 包括 11 个与生产率、环境和安全相关的基于人工智能的元宇宙内容模块,可对实际港口物流流程进行监控、模拟和决策制定。这些模块的例子包括预测预计到达时间、动态港口运营规划、监控和预测船舶燃料消耗和港口设备排放,以及检测和监控危险船舶航线和工人与港口设备之间的事故。我们利用釜山港的历史数据进行了一项案例研究,以分析 PLMF 的有效性。通过预测 PLMF 内船舶的预计抵达时间并相应优化港口运营,我们发现该框架每年可带来约 730 万美元的额外直接收入,同时船舶准点率提高了 79%,为港口带来了一定的环境效益。这些发现表明,PLMF 不仅为港口物流的各利益相关方提供了一个参与和合作的平台,而且通过基于人工智能的模拟,大大提高了港口物流决策的准确性和可持续性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence-based Smart Port Logistics Metaverse for Enhancing Productivity, Environment, and Safety in Port Logistics: A Case Study of Busan Port
The increase in global trade, the impact of COVID-19, and the tightening of environmental and safety regulations have brought significant changes to the maritime transportation market. To address these challenges, the port logistics sector is rapidly adopting advanced technologies such as big data, Internet of Things, and AI. However, despite these efforts, solving several issues related to productivity, environment, and safety in the port logistics sector requires collaboration among various stakeholders. In this study, we introduce an AI-based port logistics metaverse framework (PLMF) that facilitates communication, data sharing, and decision-making among diverse stakeholders in port logistics. The developed PLMF includes 11 AI-based metaverse content modules related to productivity, environment, and safety, enabling the monitoring, simulation, and decision making of real port logistics processes. Examples of these modules include the prediction of expected time of arrival, dynamic port operation planning, monitoring and prediction of ship fuel consumption and port equipment emissions, and detection and monitoring of hazardous ship routes and accidents between workers and port equipment. We conducted a case study using historical data from Busan Port to analyze the effectiveness of the PLMF. By predicting the expected arrival time of ships within the PLMF and optimizing port operations accordingly, we observed that the framework could generate additional direct revenue of approximately 7.3 million dollars annually, along with a 79% improvement in ship punctuality, resulting in certain environmental benefits for the port. These findings indicate that PLMF not only provides a platform for various stakeholders in port logistics to participate and collaborate but also significantly enhances the accuracy and sustainability of decision-making in port logistics through AI-based simulations.
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